Plot Shapley values-based spatial variable dependence maps
using ggplot2 by optionally setting target variable(s). This only works for
SHAPSpatial even though it is part of SpatialResponse.
# S3 method for SHAPSpatial
plot(x, target_var = NA, ...)(SHAPSpatial) The spatial variable dependence object to plot.
It could be the return of function shap_spatial_response.
(vector of character) The target variable to plot.
It could be NA. If it is NA, all variables will be plotted.
Not used.
ggplot2 figure of dependent maps
# \donttest{
# Using a pseudo presence-only occurrence dataset of
# virtual species provided in this package
library(dplyr)
library(sf)
library(stars)
library(itsdm)
# Prepare data
data("occ_virtual_species")
obs_df <- occ_virtual_species %>% filter(usage == "train")
eval_df <- occ_virtual_species %>% filter(usage == "eval")
x_col <- "x"
y_col <- "y"
obs_col <- "observation"
# Format the observations
obs_train_eval <- format_observation(
obs_df = obs_df, eval_df = eval_df,
x_col = x_col, y_col = y_col, obs_col = obs_col,
obs_type = "presence_only")
env_vars <- system.file(
'extdata/bioclim_tanzania_10min.tif',
package = 'itsdm') %>% read_stars() %>%
slice('band', c(1, 5, 12))
# With imperfect_presence mode,
mod <- isotree_po(
obs_mode = "imperfect_presence",
obs = obs_train_eval$obs,
obs_ind_eval = obs_train_eval$eval,
variables = env_vars, ntrees = 20,
sample_size = 0.8, ndim = 2L,
seed = 123L, response = FALSE,
spatial_response = FALSE,
check_variable = FALSE)
shap_spatial <- shap_spatial_response(
model = mod$model,
target_vars = c("bio1", "bio12"),
var_occ = mod$vars_train,
variables = mod$variables,
shap_nsim = 1)
plot(shap_spatial)
plot(shap_spatial, target_var = "bio1")
# }